Universal transformers

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a sequence to sequence model that is recurrent in depth while employing self-attention to combine information from different parts of sequences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/417,587, filed on May 20, 2019, which claims priority to U.S.Provisional Application No. 62/673,831, filed on May 18, 2018. Thedisclosures of the prior applications are considered part of and areincorporated by reference in the disclosure of this application.

BACKGROUND

Self-attentive feed-forward sequence models such as the Transformer havebeen shown to achieve impressive results on sequence modeling tasksincluding machine translation, image generation and constituencyparsing, presenting a compelling alternative to recurrent neuralnetworks, the de facto standard architecture for many sequence modelingproblems. Despite these successes, however, the Transformer fails togeneralize in some tasks recurrent models handle with ease. Thisincludes copying strings or simple logical inference when the string orformula lengths exceed those observed at training time.

The Transformer model is described in Vaswani et al., Attention Is AllYou Need, 31st Conference on Neural Information Processing Systems (NIPS2017), Long Beach, CA, USA, available athttps://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf. Thispaper is incorporated here by reference.

SUMMARY

This specification describes systems that implement a UniversalTransformer. Universal Transformers address, among others, theshortcomings described in the Background, above. Instead of the commonsequence-aligned recurrence, the Universal Transformer is recurrent indepth, while employing self-attention to combine information fromdifferent parts of sequences.

The Universal Transformer combines the desirable parallelizability ofself-attentive feed-forward models with an inductive bias well suited toa range of algorithmic and natural language problems. By tyingparameters across layers (depth), the Universal Transformer can be seenas iteratively refining its encoding of the sequence by applying arecurrent transformation in parallel over all symbols in the sequencefor several steps (in depth).

The Universal Transformer has additional computationally advantageousfeatures. For example, unlike the Transformer model, the UniversalTransformer is computationally universal, meaning that the model can beused to simulate any Turing machine. In addition, number ofcomputational steps of the Universal Transformer can be varieddynamically after training because the model shares weights across itssequential computational steps. Thus, the depth employed by the modelcan be scaled dynamically to the size and/or complexity of the inputsequence.

In experiments on several tasks, the Universal Transformer consistentlyimproves significantly over both a feed-forward Transformer and an LSTM(Long short-term memory) recurrent neural network.

An adaptive variant of the Universal Transformer employs an adaptivecomputation time mechanism per position in the sequence. When runningfor a fixed number of steps the Universal Transformer is equivalent to aTransformer whose parameters are tied across layers. In its adaptiveform, however, the Universal Transformer can effectively interpolatebetween the feed-forward, fixed depth Transformer and a gated, recurrentarchitecture running for a number of steps dependent on the input data.In experiments, the adaptive variant achieves state of the art resultson multiple language understanding tasks.

The Universal Transformer optionally applies a dynamic AdaptiveComputation Time (ACT) halting mechanism at each position of thesequence. ACT mechanisms are described in Graves, Adaptive computationtime for recurrent neural networks, arXiv preprint arXiv:1603.08983,2016, available at https://arxiv.org/pdf/1603.08983.pdf.

The subject matter described in this specification can be implemented inparticular embodiments so as to realize one or more of the followingadvantages.

A Universal Transformer system of the type described can be used toimplement autoregressive sequence-to-sequence models wherever suchmodels are used to generate outputs. Examples of applications ofautoregressive models include machine translation of one naturallanguage to another, summarization of natural language text, in whichthe outputs are sequences of words in sentences, speech to text, andtext to speech, involving sequences of words and phonemes. Other exampleapplications include image generation, language modeling, and parsing,e.g., constituency parsing. Other examples include applications based onsequences of images, including applications in self-driving cars androbotics control. For example, from a sequence of inputs, e.g., images,of or from a physical system, real or virtual, that includes such model,can output a sequence of actions for controlling a machine operating inor with the physical system.

The techniques can be implemented advantageously in computer systemswith GPUs and other accelerator hardware to exploit the parallelcomputational structure of the Universal Transformer.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates the operations of an exampleUniversal Transformer on a sequence.

FIG. 2 is a flowchart of an example process for encoding a sourcesequence.

FIG. 3 is a flowchart of an example process for decoding a targetsequence.

FIG. 4 illustrates an example architecture of a Universal Transformer.

FIG. 5 illustrates dynamic selection of a number of processing steps perelement in a sequence.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 is a diagram that illustrates the operations of an exampleUniversal Transformer on a sequence. The computational structureillustrated in FIG. 1 can be used to implement an encoder or a decoderon an encoder computer system or a decoder computer system having one ormore computers in one or more locations. For simplicity, the examplewill be described as being implemented on a system of one or morecomputers. As described above, the computational structure of theUniversal Transformer can be implemented on parallel processing systems,with each computational resource of a parallel processing systemperforming the operations of one or more positions in the sequence.

In general, to implement either an encoder or a decoder, the system canperform a same series of encoding or decoding operations over M sequencepositions, possibly in parallel, for T iterations. As will be describedin more detail below, in some implementations the system can adaptivelydevote more computing resources to some positions and less resources toother positions. In some implementations, the system uses the sameparameter values across all positions and all time steps.

The operations of each time step at each position can include at least aself-attention process and a transition function. For example, at stept, the system can process the first element in the sequence, the h₁representation 105 a, using a self-attention process 112 a and atransition function 114 a. The system can then update the h₁representation 105 a and repeat the same steps for T iterations.Similarly, the system can process the second element in the sequence,the h₂ representation 105 b, for T iterations using a self-attentionprocess 112 b followed by a transition function 114 b and an update ofthe h₂ representation 105 b. Likewise, the system can process the lastelement in the sequence, the h_(m) representation 105 m, for Titerations using a self-attention process 112 m followed by a transitionfunction 114 m and an update of the h_(m) representation 105 m.

Although the computations for only three sequence positions are shown inFIG. 1 , the sequence can have any arbitrary length. Therefore, toimprove computational performance the system can implement thecomputations on any appropriate parallel processing hardware. Forexample, each of the step operations can be implemented by differentstreaming multiprocessors of a GPU or different processing cores of amulticore CPU. Alternatively or in addition, each of the step operationscan be performed by different computers in a distributed system.

As shown in FIG. 1 , the self-attention processes 112 a-m for eachsequence position can use as input the current representations of othersequence positions. In other words, at each step, the system cancondition the output for a particular position on the representations sofar generated for one or more other positions. When the operations arebeing performed with parallel processing, the representations can bestored in a centrally accessible location or broadcasted after each stepto all processors performing the operations.

As will be described in more detail below, the self-attention processfor the decoder can also include a second stage attention process thatuses as input the final representations generated by the encoder.

FIG. 2 is a flowchart of an example process for encoding a sourcesequence. The example process can be performed by an appropriatelyprogrammed system of one or more computers in one or more locations. Theprocess will be described as being performed by a system of one or morecomputers.

The system receives an input sequence (210). As described above, theUniversal Transformer is widely applicable to a large collection ofsequence-to-sequence learning tasks. Thus, the input sequence can be anyappropriate input sequence of elements in a sequence-to-sequencelearning task.

Common sequence-to-sequence learning tasks include question-answeringtasks, in which case the input sequence is words in a question sentence;subject-verb agreement tasks, in which case the input sequence is wordsa natural language sentence; predicting missing target words, in whichcase the input sequence is one or more preceding natural languagesentences; algorithmic tasks, in which case the input sequence can be asequence of symbols, e.g., integers; program evaluation and memorizationtasks, in which case the input is symbols in a computer program;machine-translation tasks, in which case the input is words of a naturallanguage sentence in a first language.

The system generates respective initial representations of elements inthe input sequence (220). For example, the system can generate a vectorrepresentation for each element in the input sequence. Thus, if theinput sequence is of length m and the representations are d-dimensional,the system can initialize a matrix H⁰∈

^(m×d), with m rows, one for each item of the input sequence, with the delements of the representation of the item in the d columns of thematrix in the row. When the input elements are words, the system cangenerate the input representations that are respective word embeddingsof the words in which the word embeddings are vector representations ofthe words.

The system then repeatedly revises the representations using aself-attention process and a transition function for multiple steps.Thus, the system revises the representations (230), and determineswhether a stop-encoding condition is reached for each input element(240). In some implementations, the stop-encoding condition for eachinput element is a minimum number of revision steps T.

However, in sequence processing systems, certain symbols, e.g., somewords or phonemes, are usually more ambiguous than others. Therefore,the system can dynamically determine to allocate more processingresources to these more ambiguous symbols. For example, the system canuse Adaptive Computation Time as a mechanism for dynamically modulatingthe number of computational steps needed to process each input element.A Universal Transformer with dynamic halting thus modulates the numberof computational steps needed to process each input symbol dynamically.The number of steps can be based on a scalar pondering value that ispredicted by the model at each step. The pondering values are in a sensethe model's estimation of how much further computation is required forthe input symbols at each processing step.

Once the per-element recurrent block halts (indicating a sufficientnumber of revisions for that element), its representation is simplycopied to the next step until all blocks halt or until a maximum numberof steps is reached.

If the stop-encoding condition has not been met, the system performsanother revision step (branch to 230).

The system can apply the same series of operations iteratively on everystep by recursively applying the same series of operations. In someimplementations, the system also uses the same learned parameter valuesfor each step.

For example, at each revision step t from 1 to T, the system can computean updated representation H^(t). To do so, the system can apply amultihead dot product self-attention mechanism followed by a recurrenttransition function. In some implementations, the system also usesresidual connections around each of these computations and appliesdropout and layer normalization. In some implementations, the systemcomputes the updated representation of Ht according to:H ^(t)=LayerNorm(A ^(t−1)+Transition(A′))where A ^(t)=LayerNorm(H ^(t−1)MultiHeadSelfAttention(H ^(t−1) +P^(t))),

where the P^(t) terms are two-dimensional (position, time) coordinateembeddings, obtained by computing vertically and horizontally thesinusoidal position embedding vectors according to:P _(pos,2i) ^(t)=sin(pos/1000^(2i/d))P _(pos,2i+1) ^(t)=cos(pos/1000^(2i/d))

for the position and the time step separately (pos) for each dimension(i). The system can then sum these component-wise before applying theself-attention process.

The self-attention process can be computed as a multihead self-attentionwith k heads according to:MultiHeadSelfAttention(H)=Concat(head₁, . . . , head_(k))W ^(O)where head_(i)=Attention(HW _(i) ^(Q) ,HW _(i) ^(K) ,HW _(i) ^(V)),the projections are the following learned parameter matrices:W ^(Q)∈

^(d×d/k) ,W ^(K)∈

^(d×d/k) ,W ^(V)∈

^(d×d/k) and W ^(O)∈

^(d×d)

and the attention is a scaled dot product attention computed accordingto:

${{{Attention}\left( {Q,K,V} \right)} = {{{softmax}\left( \frac{{QK}^{T}}{\sqrt{d}} \right)}V}},$

where d is the number of columns of Q, K and V and the dimension of thesymbol representations.

The system can tailor the transition function to the particular task.For example, the system can use a separable convolution or afully-connected neural network having a single rectified-linearactivation function between two linear transformations, appliedposition-wise, e.g., individually to each row of A^(t).

After the stop-encoding condition is reached, e.g., after T steps, thesystem provides final representations to the decoder (250). In someimplementations, the final output is a matrix of vector representationsH^(T) for the input sequence.

FIG. 3 is a flowchart of an example process for decoding a targetsequence. As described above, the decoding process can share the samebasic structure in depth as the encoder process, but performs anadditional self-attention stage that uses the final encoderrepresentations. As shown by FIGS. 2 and 3 , the system can implementthe Universal Transformer by running the encoder process with T stepsonce, followed by running the decoder process with N steps eachautoregressively multiple times for each element in the target sequence.The example process can be performed by an appropriately programmedsystem of one or more computers in one or more locations. The processwill be described as being performed by a system of one or morecomputers.

The system receives an initial target sequence (310). The system cangenerate the initial target sequence according to the task at hand. Forexample, tasks related to natural language processing and machinetranslation, the system can initialize an answer sequence. For tasksrelating to algorithmic process and program evaluation, the system caninitialize a sequence of symbols.

The system revises a representation of the next predicted element in thetarget sequence using two-stage self-attention and a transition function(320). The system can use the same structure as the encoder, with afirst self-attention stage. The system can additionally apply a secondself-attention stage using the final encoder representations of theinput sequence using a multihead dot product attention function. In thesecond self-attention stage, the system can use queries Q obtained fromprojecting the decoder representations and keys K and values V obtainedfrom projecting the encoder representations. The system canautoregressively determine each next symbol in the sequence, which meansthat each output in the target sequence is conditioned on all of thepreviously generated outputs in the target sequence.

The system determines whether a stop-decoding condition is met for thenext element in the target sequence (330). In some implementations, thesystem uses a fixed number of steps N, which may or may not be equal tothe number of steps T used by the encoder process.

Alternatively or in addition, the system can adaptively alter the numberof steps in the decoding process for the next symbol depending on theposition of the next symbol. For example, the system can apply theAdaptive Computation Time (ACT) halting mechanism at each position andcopy the state to the next step after a block is halted, until allblocks are halted or the predetermined maximum number of steps have beenperformed.

If the stop-decoding condition is not met, the system again performsanother step of revisions for the next element in the target sequence(branch to 320).

Otherwise, the system determines whether there are more elements todecode (branch to 340). In some implementations, the model is trained togenerate a special end-of-sequence symbol when there are no more outputelements to be decoded. Therefore, the system can continue the processuntil the decoder generates the end-of-sequence symbol.

If there are more elements to decode, the system begins revising arepresentation of the next element in the target sequence (branch to320).

If there are no more elements to decode, the system provides a finaltransformed output sequence (branch to 350). To obtain the per-symboltarget distributions at positions 1 through n, the system can apply anaffine transformation O from the final state to an output vocabularysize, followed by the following softmax function:p(y _(pos) |y _([1:pos−1]) ,H ^(T))=softmax(OH ^(T)).During the training process, the system can perform teacher-forcedtraining in which the decoder input is the target output shifted to theright by one position. The system can further mask the decoderself-attention distributions so that the model can only attend topositions to the left of any predicted symbol.

FIG. 4 illustrates an example architecture of a Universal Transformer.As described above, the Universal Transformer includes a recurrentencoder block 410 and a recurrent decoder block 420. Each of theseblocks can be implemented using respective computing resources, and, asdescribed above, the blocks can have multiple instances for each elementin an input sequence.

For ease of illustration the encoder block 410 is illustrated in FIG. 4as receiving a source sequence 401. However, as described above, asystem can use multiple instances of the encoder block for each elementin the source sequence 401, which can be executed for T steps inparallel. Thus, the representation of the source sequence 401 in FIG. 4can be interpreted as a matrix with each row being a differentrepresentation of a different element in the source sequence 401.

In operation, the encoder block 401 can receive the source sequence 401and add position and timestep embeddings. The encoder block 410 can thenperform a multihead self-attention process 411, followed by layers ofdropout 412 and layer normalization 413. The encoder block 410 can thenperform a transition function 414 followed by another layer of dropout415 and layer normalization 416.

The encoder block then repeats these operations for T steps, each timerevising the representation of each element in the source sequence 401.After T steps, the encoder block 410 provides the final representationsof the elements in the source sequence 401 to the decoder block 420. Asdescribed above, the number of steps for each input element can differ.For elements whose encoding block has halted, the system can simply copythe halted representation to the next step.

For ease of illustration, the decoder block 420 is illustrated in FIG. 4as receiving a target sequence 402. However, as described above, asystem can use multiple instances of the decoder block for multiplesteps for each element so far generated in the target sequence. In otherwords, the number of decoder blocks can grow as more elements aregenerated in the target sequence. The system can execute each of thedecoder blocks for each of the elements in the target sequence inparallel.

In operation, the decoder block 420 can receive the target sequence 402and add position and timestep embeddings. The decoder block 420 can thenperform a multihead self-attention process 421, followed by layers ofdropout 422 and layer normalization 423.

The decoder block 420 can then perform a second stage of attention, witha multihead attention process 424 conditioned on the finalrepresentations generated by the encoder block 410.

The decoder block 420 can then perform additional layers of dropout 425and layer normalization 426 before performing a transition function 427.The decoder block 420 can perform final layers of dropout 428 and layernormalization 429.

The decoder block 420 can repeat these operations for multiple steps foreach element in the target sequence. As described above, the number ofsteps can be dynamically selected by the model for each position to bedecoded.

After the N steps of decoding have completed, the system applies a finalsoftmax layer 430 to generate final output probabilities 440.

FIG. 5 illustrates dynamic selection of a number of processing steps perelement in a sequence. As shown, the sequence has four elements, eachwith a respective initial representation 505 a, 505 b, 505 c, and 505 d.

At step 1, the system can generate in parallel four revisedrepresentations using respective encoder or decoder blocks for eachposition.

At step 2, the block for position 4 halts, while the system generatesthree revised representations for the other positions.

At step 3, the system generates three revised representations for thethree positions that have not yet halted.

At step 4, the block for position 1 halts. The system generates tworevised representations for the other positions that have not halted atposition 2 and position 3.

The dashed lines in FIG. 5 represent the inputs to the self-attentionprocess used to generate the revised representations. Thus, at step 4,the system generates the revised representation for position 3 by usingthe representations of the other positions as input to theself-attention process. Notably, as shown in FIG. 5 the system uses thehalted representation for position 4 from step 2, while usingrepresentations from step 3 for positions 1 and 2. In other words, thesystem can execute the self-attention process by using representationsgenerated in different steps at different times.

At step 5, the block for position 3 halts, and the system generates arevised representation only for position 2.

Similarly, at step 6, the system generates a revised representation forposition 2 using representations generated on three different steps: therepresentation from step 5 for position 3, the representation from step4 for position 1, and the representation from step 2 for position 4.

The final output of the process is then the collection of the finalhalted representations at each position.

As described above, systems and methods described herein may be appliedto a variety of technical tasks, a few of which will now be described inmore detail.

For example, for question-answering tasks the goal is to answer aquestion given a number of English sentences that encode potentiallymultiple supporting facts. The goal is to measure various forms oflanguage understanding by requiring a certain type of reasoning over thelinguistic facts presented in each story. A standard Transformer doesnot achieve good results on this type of task. To encode the input, thesystem can first encode each fact in the story by applying a learnedmultiplicative positional mask to each word's embedding, and summing upall embeddings. Models can either be trained on each task separately(“train single”) or jointly on all tasks (“train joint”). The system canembed the question in the same way, and then feed the UniversalTransformer with these embeddings of the facts and questions. Over 10runs with different initializations and using the best model based onperformance on the validation set, both the Universal Transformer andUniversal Transformer with dynamic halting achieve state-of-the-artresults on all tasks in terms of average error and number of failedtasks. TABLE 1 summarizes the results.

TABLE 1 Model 10K examples 1K examples Previous Best Results QRNet (Seo0.3 (0/20) et al., 2016) Sparse DNC 2.9 (1/20) (Rae et al., 2016) GA +MAGE  8.7 (5/20) (Dhingra et al. 2017) MemN2N 12.4 (11/20) (Sukhbaataret al. 2015) Transformer 15.2 (10/20) 22.1 (12/20) 21.8 (5/20) 26.8(14/20) (Vaswani et al., 2017) Universal Transformer Results Universal0.23 (0/20)  0.47 (0/20)  5.31 (5/20) 8.50 (8/20)  Transformer Universal0.21 (0/20)  0.29 (0/20)  4.55 (3/20) 7.78 (5/20)  Transformer withdynamic halting

For subject-verb agreement tasks, the goal is to predictnumber-agreement between subjects and verbs in English. This task actsas a proxy for measuring the ability of a model to capture hierarchicaldependency structure in natural language sentences. The system can use alanguage modeling training setup, i.e., a next word predictionobjective, followed by calculating the ranking accuracy of the targetverb at test time. The Universal Transformer was evaluated on subsets ofthe test data with different task difficulty, measured in terms ofagreement attractors—the number of intervening nouns with the oppositenumber from the subject (meant to confuse the model). For example, giventhe sentence, “The keys to the cabinet,” the objective during trainingis to predict the verb are (plural). At test time, the ranking accuracyof the agreement attractors is evaluated: i.e. the goal is to rank “are”higher than is in this case. The best LSTM with attention from theliterature achieves 99.18% on this task, which outperforms a regularTransformer. The Universal Transformer significantly outperformsstandard Transformers and achieves an average result comparable to thecurrent state of the art (99.2%). However, the Universal Transformers(and particularly with dynamic halting) perform progressively betterthan all other models as the number of attractors increases. TABLE 2summarizes the results.

TABLE 2 Number of Attractors Model 0 1 2 3 4 5 Total Previous bestresults (Yogatama et al., 2018) Best Stack- 0.994 0.979 0.965 0.9350.916 0.880 0.992 RNN Best LSTM 0.993 0.972 0.950 0.922 0.900 0.8420.991 Best 0.994 0.977 0.959 0.929 0.907 0.842 0.992 Attention UniversalTransformer Universal 0.993 0.971 0.969 0.940 0.921 0.892 0.992Transformer Universal 0.994 0.969 0.967 0.944 0.932 0.907 0.992Transformer with dynamic halting

For language modeling tasks, the goal is to predict missing targetwords, in which case the input sequence is one or more preceding naturallanguage sentences. The dataset was specifically designed so that humansare able to accurately predict the target word when shown the fullcontext, but not when only shown the target sentence in which itappears. It therefore goes beyond language modeling and tests theability of a model to incorporate broader discourse and longer termcontext when predicting the target word. The task was evaluated in twosettings: as language modeling (the standard setup) and as readingcomprehension. In the former (more challenging) case, a model was simplytrained for next-word prediction on the training data, and evaluated onthe target words at test time. In other words, the model was trained topredict all words, not specifically challenging target words. In thelatter setting, the target sentence (minus the last word) was used asquery for selecting the target word from the context sentences. In thistask, the Universal Transformer achieves state-of-the-art results inboth the language modeling and reading comprehension setup,outperforming both LSTMs and normal Transformers. In this experiment,the control set was constructed similar to the language modelingdevelopment and test sets, but without filtering them in any way, soachieving good results on this set shows a model's strength in standardlanguage modeling.

For algorithmic tasks, the goal is to perform a symbol transformationfrom one sequence to another, e.g., Copy, Reverse, and integer Addition.In an experiment in which the model was trained with sequences of length40 and evaluated on sequences of length 400, the system trainedUniversal Transformers using positions starting with randomized offsetsto further encourage the model to learn position-relativetransformations. The Universal Transformer outperformed both LSTM andthe normal Transformer by a wide margin on all three tasks.

For learning-to-execute tasks, e.g., program evaluation andmemorization, the Universal Transformer was evaluated on tasksindicating the ability of a model to learn to execute computer programs.These tasks include program evaluation tasks (program, control, andaddition), and memorization tasks (copy, double, and reverse). Withoutusing any curriculum learning strategy during training and without usingtarget sequences at test time, the Universal Transformer achievesperfect scores in all the memorization tasks and also outperforms bothLSTMs and Transformers in all program evaluation tasks by a wide margin.

For machine translation, the Universal Transformer was evaluated bytraining an English-German translation task. The Universal Transformerwith a fully-connected recurrent transition function (instead ofseparable convolution) and without ACT improves by 0.9 BLEU over aTransformer and 0.5 BLEU over a Weighted Transformer with approximatelythe same number of parameters.

The Universal Transformer can also be adapted for use on othersequence-to-sequence tasks. One example application is speech-to-textconversion, in which case the input sequence is representations of soundwaves and the output sequence is text in a particular language. Anotherexample is text-to-speech conversion, in which case the input sequenceis text, and the output sequence is representations of sound waves.Another example application is image generation. Another exampleapplication is robotic control.

For example, in some implementations, the sequence of inputs maycomprise images (e.g., images comprising pixel data), for instanceimages of or from a physical system, and the corresponding output mayinclude a sequence of actions for controlling a machine or robot, whichmay operate in or with the physical system.

Embodiments of the subject matter and the actions and operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer storage mediumcan be or be part of a machine-readable storage device, amachine-readable storage substrate, a random or serial access memorydevice, or a combination of one or more of them. A computer storagemedium is not a propagated signal.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.Data processing apparatus can include special-purpose logic circuitry,e.g., an FPGA (field programmable gate array), an ASIC(application-specific integrated circuit), or a GPU (graphics processingunit). The apparatus can also include, in addition to hardware, codethat creates an execution environment for computer programs, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, an engine, a script, or code, can be written in any form ofprogramming language, including compiled or interpreted languages, ordeclarative or procedural languages; and it can be deployed in any form,including as a stand-alone program or as a module, component, engine,subroutine, or other unit suitable for executing in a computingenvironment, which environment may include one or more computersinterconnected by a data communication network in one or more locations.

A computer program may, but need not, correspond to a file in a filesystem. A computer program can be stored in a portion of a file thatholds other programs or data, e.g., one or more scripts stored in amarkup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files, e.g., files that store oneor more modules, sub-programs, or portions of code.

The processes and logic flows described in this specification can beperformed by one or more computers executing one or more computerprograms to perform operations by operating on input data and generatingoutput. The processes and logic flows can also be performed byspecial-purpose logic circuitry, e.g., an FPGA, an ASIC, or a GPU, or bya combination of special-purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special-purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for executing instructions and one or more memorydevices for storing instructions and data. The central processing unitand the memory can be supplemented by, or incorporated in,special-purpose logic circuitry.

Generally, a computer will also include, or be operatively coupled toreceive data from or transfer data to one or more mass storage devices.The mass storage devices can be, for example, magnetic, magneto-optical,or optical disks, or solid state drives. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on, orconfigured to communicate with, a computer having a display device,e.g., a LCD (liquid crystal display) monitor, for displaying informationto the user, and an input device by which the user can provide input tothe computer, e.g., a keyboard and a pointing device, e.g., a mouse, atrackball or touchpad. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents toand receiving documents from a device that is used by the user; forexample, by sending web pages to a web browser on a user's device inresponse to requests received from the web browser, or by interactingwith an app running on a user device, e.g., a smartphone or electronictablet. Also, a computer can interact with a user by sending textmessages or other forms of message to a personal device, e.g., asmartphone that is running a messaging application, and receivingresponsive messages from the user in return.

This specification uses the term “configured to” in connection withsystems, apparatus, and computer program components. For a system of oneor more computers to be configured to perform particular operations oractions means that the system has installed on it software, firmware,hardware, or a combination of them that in operation cause the system toperform the operations or actions. For one or more computer programs tobe configured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions. For special-purpose logic circuitry to be configured to performparticular operations or actions means that the circuitry has electroniclogic that performs the operations or actions.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what is being or may be claimed, but ratheras descriptions of features that may be specific to particularembodiments of particular inventions. Certain features that aredescribed in this specification in the context of separate embodimentscan also be implemented in combination in a single embodiment.Conversely, various features that are described in the context of asingle embodiment can also be implemented in multiple embodimentsseparately or in any suitable subcombination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially be claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaim may be directed to a subcombination or variation of asubcombination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. Generally, multitasking andparallel processing may be advantageous.

What is claimed is:
 1. A method performed by one or more computers, themethod comprising: receiving an input sequence of elements to betransformed into an output sequence of elements according to a learnedtransformation, each element of the input sequence having a respectiveinitial input representation; performing an encoding process includingrepeatedly revising the input representations in parallel using a sameseries of encoding operations for each of multiple time steps;initializing a target sequence of elements each having a respectiveinitial target representation; performing a decoding process includingrepeatedly revising the target representations in parallel usingtwo-stage self-attention, wherein a second stage of the two-stageself-attention uses an output generated by the encoding process afterrepeatedly revising the input representations; and generating, from afinal version of the revised target representations, the output sequenceof elements representing the learned transformation of the inputsequence of elements.
 2. The method of claim 1, wherein performing thedecoding process comprises revising the target representations includingconditioning on previous representations generated by the decodingprocess and on the output generated by the encoding process.
 3. Themethod of claim 1, wherein encoding process uses only one stage ofself-attention.
 4. The method of claim 1, wherein the encoding processuses a transition function comprising a fully-connected neural networklayer having a single rectified-linear activation function between twolinear transformations.
 5. The method of claim 1, wherein the encodingprocess uses a transition function that is a separable convolution. 6.The method of claim 1, wherein the encoding process and the decodingprocess both use neural networks that are recurrent in depth.
 7. Themethod of claim 1, wherein the input sequence of elements represents aquestion and the target sequence of elements represents an answer to thequestion.
 8. The method of claim 1, wherein the input sequence ofelements represents a subject of a sentence and the target sequencerepresents a predicted verb form for the subject of the sentence.
 9. Themethod of claim 1, wherein the input sequence of elements representswords in a sentence and the target sequence represents a predicted nextword in the sentence.
 10. The method of claim 1, wherein thetransformation is a symbol transformation for an algorithmic task. 11.The method of claim 1, wherein the input sequence represents words in afirst language and the target sequence represents a translated versionof the input sequence in a second language.
 12. A system comprising: oneor more computers and one or more storage devices storing instructionsthat are operable, when executed by the one or more computers, to causethe one or more computers to perform operations comprising: receiving aninput sequence of elements to be transformed into an output sequence ofelements according to a learned transformation, each element of theinput sequence having a respective initial input representation;performing an encoding process including repeatedly revising the inputrepresentations in parallel using a same series of encoding operationsfor each of multiple time steps; initializing a target sequence ofelements each having a respective initial target representation;performing a decoding process including repeatedly revising the targetrepresentations in parallel using two-stage self-attention, wherein asecond stage of the two-stage self-attention uses an output generated bythe encoding process after repeatedly revising the inputrepresentations; and generating, from a final version of the revisedtarget representations, the output sequence of elements representing thelearned transformation of the input sequence of elements.
 13. The systemof claim 12, wherein performing the decoding process comprises revisingthe target representations including conditioning on previousrepresentations generated by the decoding process and on the outputgenerated by the encoding process.
 14. The system of claim 12, whereinencoding process uses only one stage of self-attention.
 15. The systemof claim 12, wherein the encoding process uses a transition functioncomprising a fully-connected neural network layer having a singlerectified-linear activation function between two linear transformations.16. The system of claim 12, wherein the encoding process uses atransition function that is a separable convolution.
 17. The system ofclaim 12, wherein the encoding process and the decoding process both useneural networks that are recurrent in depth.
 18. The system of claim 12,wherein the input sequence of elements represents a question and thetarget sequence of elements represents an answer to the question. 19.The system of claim 12, wherein the input sequence of elementsrepresents a subject of a sentence and the target sequence represents apredicted verb form for the subject of the sentence.
 20. One or morenon-transitory computer storage media encoded with computer programinstructions that when executed by one or more computers cause the oneor more computers to perform operations comprising: receiving an inputsequence of elements to be transformed into an output sequence ofelements according to a learned transformation, each element of theinput sequence having a respective initial input representation;performing an encoding process including repeatedly revising the inputrepresentations in parallel using a same series of encoding operationsfor each of multiple time steps; initializing a target sequence ofelements each having a respective initial target representation;performing a decoding process including repeatedly revising the targetrepresentations in parallel using two-stage self-attention, wherein asecond stage of the two-stage self-attention uses an output generated bythe encoding process after repeatedly revising the inputrepresentations; and generating, from a final version of the revisedtarget representations, the output sequence of elements representing thelearned transformation of the input sequence of elements.